Sponsor
This study was supported by grants from the Swedish Research Council (2014-3561), A-K W, (https://www.vr.se/english.html). The Swedish Research Council had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Published In
Plos One
Document Type
Article
Publication Date
2020
Subjects
Preeclampsia -- Clinical trials, Miscarriage -- Immunological aspects Preeclampsia -- Immunological aspects
Abstract
Objective
To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women.
Design and setting
A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in the Stockholm-Gotland Counties in Sweden.
Methods
Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery
Results
A total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with deliveryweeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5–37.2) and 29.2% (95% CI 25.2–33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery≥37 weeks’ gestation.
Conclusion
Multivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.
Rights
Copyright: © 2019 Sandstro¨m et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Locate the Document
DOI
10.1371/journal.pone.0225716
Persistent Identifier
https://archives.pdx.edu/ds/psu/32650
Publisher
Plos One
Citation Details
Sandström, A., Snowden, J. M., Höijer, J., Bottai, M., & Wikström, A. K. (2019). Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study. PloS one, 14(11).